Clinical summarization is the act of collecting, distilling, and synthesizing patient information for the purpose of facilitating any of a wide range of clinical tasks. Four categories of clinical summaries are defined as: Extractive Summaries which are created by borrowing unaltered text; Abstract Summaries which generate new text based upon synthesis and each category can be extended by a further dimension; Indicative Summaries which point to important parts of the text to provide highlights of significant information; and Informative Summaries which replace the patient record and can be used as a replacement of all the original data.
Prior approaches to generate a clinical summary have typically relied on nurses in the hospital working through the entire record set manually and copying relevant data into an excel spreadsheet. This clinical summarization is then presented to the physician for review during the first patient appointment. Unfortunately, this process of generating these prior clinical summarizations is inefficient, time consuming, and often may miss relevant data negatively impacting patient care.
Additionally, in other non-medical related fields natural language processing algorithms have been used to process and analyze text. Although helpful these natural language processing algorithms have had computer processing issues in accurately and effectively processing and understanding complicated text. As a result, when these prior natural language processing algorithms have been applied, inferior results are often obtained.